The most helpful favourable review

The most helpful critical review

2 of 2 people found the following review helpful

5.0 out of 5 starsVery accessible
This is the textbook I'm using for an undergraduate machine learning course, and so far it has been very enjoyable. There are plenty of exercises in each chapter, from simple "derive a formula for ..." to more in depth problems, and several of the problems have solutions. One thing I've found really useful is how well referenced the book is within itself. It'll say...

2.0 out of 5 starsClaims to use very little math, but not. Also hard to follow
I've found this book a disaster for my Machine Learning course. It claims to use as little math as possible in the introduction, but as anyone that owns the book would tell you, a chapter could easily gather 200+ equations, and students would require pretty advanced calculus to actually have a clue on what the math's about.The book does a poor job at conveying the...

This review is from: Pattern Recognition and Machine Learning (Hardcover)

This is the textbook I'm using for an undergraduate machine learning course, and so far it has been very enjoyable. There are plenty of exercises in each chapter, from simple "derive a formula for ..." to more in depth problems, and several of the problems have solutions. One thing I've found really useful is how well referenced the book is within itself. It'll say something like "Recall that, if we assume a squared loss function, then the optimal prediction, for a new value of x, will be given by the conditional mean of the target variable". In the margin, it then has in red the text "Section 1.5.5" pointing you to where we learned this bit of trivia. Formatting is also well done, charts are colourful and seem to get the point across well. I find that I learn much easier by being shown a picture/graph of what we want to achieve, and then have it described, and finally being given the equations for solving this (rather than just being given the equations), which this book does well. It has taken a fair amount of work to get through though, and so I wouldn't say it's an easy textbook by any means (I mean, come on, we're teaching computers how to think, that can't be easy).

My one complaint is that I wish that they had a chapter/appendix with a bit of a stats refresher, because the last stats course I took was over a year ago, and so this textbook took me a little bit to get into for lack of knowing what some of the early terms meant.

This review is from: Pattern Recognition and Machine Learning (Hardcover)

This books provides an excellent introduction to a wide range of techniques in machine learning and also serves as a good reference.

However, it does require the reader to be mathematically mature, otherwise it can take some time to read through. It is probably best for someone at the graduate level (who has already taken a couple of graduate courses).

This review is from: Pattern Recognition and Machine Learning (Hardcover)

I've found this book a disaster for my Machine Learning course. It claims to use as little math as possible in the introduction, but as anyone that owns the book would tell you, a chapter could easily gather 200+ equations, and students would require pretty advanced calculus to actually have a clue on what the math's about.The book does a poor job at conveying the ideas across, and although the professor reuses lots of graphics, his trimmed down notes were much more useful at an attempt to understand the topic.Personally I found the book a waste unless you already have some understanding of machine learning in the first place. Beginners need not apply.